15 results on '"Donald, Bruce"'
Search Results
2. LUTE (Local Unpruned Tuple Expansion): Accurate Continuously Flexible Protein Design with General Energy Functions and Rigid-rotamer-like Efficiency
- Author
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Hallen, Mark A., Jou, Jonathan D., Donald, Bruce R., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Josef, Kittler, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, and Singh, Mona, editor
- Published
- 2016
- Full Text
- View/download PDF
3. Comets (Constrained Optimization of Multistate Energies by Tree Search): A Provable and Efficient Algorithm to Optimize Binding Affinity and Specificity with Respect to Sequence
- Author
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Hallen, Mark A., Donald, Bruce R., Istrail, Sorin, Series editor, Pevzner, Pavel, Series editor, Waterman, Michael, Series editor, and Przytycka, Teresa M., editor
- Published
- 2015
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4. Protein design algorithms predict viable resistance to an experimental antifolate
- Author
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Reeve, Stephanie M., Gainza, Pablo, Frey, Kathleen M., Georgiev, Ivelin, Donald, Bruce R., and Anderson, Amy C.
- Published
- 2015
5. Computational Structure-Based Redesign of Enzyme Activity
- Author
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Chen, Cheng-Yu, Georgiev, Ivelin, Anderson, Amy C., Donald, Bruce R., and Richardson, Jane S.
- Published
- 2009
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6. OSPREY 3.0: Open‐source protein redesign for you, with powerful new features.
- Author
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Hallen, Mark A., Martin, Jeffrey W., Ojewole, Adegoke, Jou, Jonathan D., Lowegard, Anna U., Frenkel, Marcel S., Gainza, Pablo, Nisonoff, Hunter M., Mukund, Aditya, Wang, Siyu, Holt, Graham T., Zhou, David, Dowd, Elizabeth, and Donald, Bruce R.
- Subjects
PROTEIN engineering ,GRAPHICS processing units ,OPEN source software ,PYTHON programming language ,COMPUTER algorithms - Abstract
We present osprey 3.0, a new and greatly improved release of the osprey protein design software. Osprey 3.0 features a convenient new Python interface, which greatly improves its ease of use. It is over two orders of magnitude faster than previous versions of osprey when running the same algorithms on the same hardware. Moreover, osprey 3.0 includes several new algorithms, which introduce substantial speedups as well as improved biophysical modeling. It also includes GPU support, which provides an additional speedup of over an order of magnitude. Like previous versions of osprey, osprey 3.0 offers a unique package of advantages over other design software, including provable design algorithms that account for continuous flexibility during design and model conformational entropy. Finally, we show here empirically that osprey 3.0 accurately predicts the effect of mutations on protein–protein binding. Osprey 3.0 is available at http://www.cs.duke.edu/donaldlab/osprey.php as free and open‐source software. © 2018 Wiley Periodicals, Inc. We present the third major release of the OSPREY protein design software, along with comparisons to experimental data that confirm its ability to optimize protein mutants for desired functions. Osprey 3.0 has significant efficiency, ease‐of‐use, and algorithmic improvements over previous versions, including GPU acceleration and a new Python interface. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
7. Improved HIV-1 neutralization breadth and potency of V2-apex antibodies by in silico design.
- Author
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Holt, Graham T., Gorman, Jason, Wang, Siyu, Lowegard, Anna U., Zhang, Baoshan, Liu, Tracy, Lin, Bob C., Louder, Mark K., Frenkel, Marcel S., McKee, Krisha, O'Dell, Sijy, Rawi, Reda, Shen, Chen-Hsiang, Doria-Rose, Nicole A., Kwong, Peter D., and Donald, Bruce R.
- Abstract
Broadly neutralizing antibodies (bNAbs) against HIV can reduce viral transmission in humans, but an effective therapeutic will require unusually high breadth and potency of neutralization. We employ the OSPREY computational protein design software to engineer variants of two apex-directed bNAbs, PGT145 and PG9RSH, resulting in increases in potency of over 100-fold against some viruses. The top designed variants improve neutralization breadth from 39% to 54% at clinically relevant concentrations (IC 80 < 1 μg/mL) and improve median potency (IC 80) by up to 4-fold over a cross-clade panel of 208 strains. To investigate the mechanisms of improvement, we determine cryoelectron microscopy structures of each variant in complex with the HIV envelope trimer. Surprisingly, we find the largest increases in breadth to be a result of optimizing side-chain interactions with highly variable epitope residues. These results provide insight into mechanisms of neutralization breadth and inform strategies for antibody design and improvement. [Display omitted] • Antibody variants designed using OSPREY improve neutralization breadth and potency • Structural and statistical analyses highlight improved side-chain interactions • Improvements in breadth result from interaction with variable epitope residues Broadly neutralizing antibodies against HIV are promising therapeutics and targets for vaccine elicitation. Using the OSPREY design software, Holt et al. design antibody variants with improved breadth and potency of virus neutralization. They solve bound structures for these variants and provide insight into mechanisms of breadth and potency. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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8. Computational structure-based redesign of enzyme activity.
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Cheng-Yu Chen, Georgiev, Ivelin, Anderson, Amy C., and Donald, Bruce R.
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ENZYME activation ,ENZYMES ,PHENYLALANINE ,GRAMICIDINS ,LIGASES ,PROTEIN engineering - Abstract
We report a computational, structure-based redesign of the phenylalanine adenylation domain of the nonribosomal peptide synthetase enzyme gramicidin S synthetase A (GrsA-PheA) for a set of noncognate substrates for which the wild-type enzyme has little or virtually no specificity. Experimental validation of a set of top- ranked computationally predicted enzyme mutants shows significant improvement in the specificity for the target substrates. We further present enhancements to the methodology for computational enzyme redesign that are experimentally shown to result in significant additional improvements in the target substrate specificity. The mutant with the highest activity for a noncognate substrate exhibits 1/6 of the wild-type enzyme/wild-type substrate activity, further confirming the feasibility of our computational approach. Our results suggest that structure-based protein design can identify active mutants different from those selected by evolution. [ABSTRACT FROM AUTHOR]
- Published
- 2009
- Full Text
- View/download PDF
9. The minimized dead-end elimination criterion and its application to protein redesign in a hybrid scoring and search algorithm for computing partition functions over molecular ensembles.
- Author
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Georgiev, Ivelin, Lilien, Ryan H., and Donald, Bruce R.
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PROTEIN engineering ,PROTEINS ,LIGAND binding (Biochemistry) ,CONFORMATIONAL analysis ,ALGORITHMS - Abstract
One of the main challenges for protein redesign is the efficient evaluation of a combinatorial number of candidate structures. The modeling of protein flexibility, typically by using a rotamer library of commonly-observed low-energy side-chain conformations, further increases the complexity of the redesign problem. A dominant algorithm for protein redesign is dead-end elimination (DEE), which prunes the majority of candidate conformations by eliminating rigid rotamers that provably are not part of the global minimum energy conformation (GMEC). The identified GMEC consists of rigid rotamers (i.e., rotamers that have not been energy-minimized) and is thus referred to as the rigid-GMEC. As a postprocessing step, the conformations that survive DEE may be energy-minimized. When energy minimization is performed after pruning with DEE, the combined protein design process becomes heuristic, and is no longer provably accurate: a conformation that is pruned using rigid-rotamer energies may subsequently minimize to a lower energy than the rigid-GMEC. That is, the rigid-GMEC and the conformation with the lowest energy among all energy-minimized conformations (the minimized-GMEC) are likely to be different. While the traditional DEE algorithm succeeds in not pruning rotamers that are part of the rigid-GMEC, it makes no guarantees regarding the identification of the minimized-GMEC. In this paper we derive a novel, provable, and efficient DEE-like algorithm, called minimized-DEE (MinDEE), that guarantees that rotamers belonging to the minimized-GMEC will not be pruned, while still pruning a combinatorial number of conformations. We show that MinDEE is useful not only in identifying the minimized-GMEC, but also as a filter in an ensemble-based scoring and search algorithm for protein redesign that exploits energy-minimized conformations. We compare our results both to our previous computational predictions of protein designs and to biological activity assays of predicted protein mutants. Our provable and efficient minimized-DEE algorithm is applicable in protein redesign, protein-ligand binding prediction, and computer-aided drug design. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2008 [ABSTRACT FROM AUTHOR]
- Published
- 2008
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10. BBK* (Branch and Bound Over K*): A Provable and Efficient Ensemble-Based Protein Design Algorithm to Optimize Stability and Binding Affinity Over Large Sequence Spaces.
- Author
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Jou, Jonathan D., Ojewole, Adegoke A., Donald, Bruce R., and Fowler, Vance G.
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PROTEIN engineering , *ALGORITHMS , *PROTEIN binding , *SEQUENCE spaces , *STRUCTURAL bioinformatics , *STRUCTURAL stability - Abstract
Computational protein design (CPD) algorithms that compute binding affinity,
Ka , search for sequences with an energetically favorable free energy of binding. Recent work shows that three principles improve the biological accuracy of CPD: ensemble-based design , continuous flexibility of backbone and side-chain conformations, and provable guarantees of accuracy with respect to the input. However, previous methods that use all three design principles are single-sequence (SS) algorithms, which are very costly: linear in the number of sequences and thus exponential in the number of simultaneously mutable residues. To address this computational challenge, we introduce BBK* , a new CPD algorithm whose key innovation is the multisequence (MS) bound: BBK* efficiently computes a single provable upper bound to approximateKa for a combinatorial number of sequences , and avoids SS computation for all provably suboptimal sequences. Thus, to our knowledge, BBK* is the first provable, ensemble-based CPD algorithm to run in time sublinear in the number of sequences. Computational experiments on 204 protein design problems show that BBK* finds the tightest binding sequences while approximatingKa for up to 105-fold fewer sequences than the previous state-of-the-art algorithms, which require exhaustive enumeration of sequences. Furthermore, for 51 protein–ligand design problems, BBK* provably approximatesKa up to 1982-fold faster than the previous state-of-the-art iMinDEE/ \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $${\curr A^*}$$ \end{document} / \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} $${\curr K^*}$$ \end{document} algorithm. Therefore, BBK* not only accelerates protein designs that are possible with previous provable algorithms, but also efficiently performs designs that are too large for previous methods. [ABSTRACT FROM AUTHOR]- Published
- 2018
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11. cOSPREY: A Cloud-Based Distributed Algorithm for Large-Scale Computational Protein Design.
- Author
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YUCHAO PAN, YUXI DONG, JINGTIAN ZHOU, MARK HALLEN, DONALD, BRUCE R., JIANYANG ZENG, and WEI XU
- Subjects
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PROTEIN engineering , *CLOUD computing , *DISTRIBUTED algorithms , *PROTEIN conformation , *COMPUTATIONAL biology - Abstract
Finding the global minimum energy conformation (GMEC) of a huge combinatorial search space is the key challenge in computational protein design (CPD) problems. Traditional algorithms lack a scalable and efficient distributed design scheme, preventing researchers from taking full advantage of current cloud infrastructures. We design cloud OSPREY (cOSPREY), an extension to a widely used protein design software OSPREY, to allow the original design framework to scale to the commercial cloud infrastructures. We propose several novel designs to integrate both algorithm and system optimizations, such as GMEC-specific pruning, state search partitioning, asynchronous algorithm state sharing, and fault tolerance. We evaluate cOSPREY on three different cloud platforms using different technologies and show that it can solve a number of large-scale protein design problems that have not been possible with previous approaches. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
12. BWM*: A Novel, Provable, Ensemble-based Dynamic Programming Algorithm for Sparse Approximations of Computational Protein Design.
- Author
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JOU, JONATHAN D., JAIN, SWATI, GEORGIEV, IVELIN S., and DONALD, BRUCE R.
- Subjects
- *
DYNAMIC programming , *PROTEIN engineering , *PROTEIN conformation , *SPARSE approximations , *SUBSTITUENTS (Chemistry) - Abstract
Sparse energy functions that ignore long range interactions between residue pairs are frequently used by protein design algorithms to reduce computational cost. Current dynamic programming algorithms that fully exploit the optimal substructure produced by these energy functions only compute the GMEC. This disproportionately favors the sequence of a single, static conformation and overlooks better binding sequences with multiple low-energy conformations. Provable, ensemble-based algorithms such as A* avoid this problem, but A* cannot guarantee better performance than exhaustive enumeration. We propose a novel, provable, dynamic programming algorithm called Branch-Width Minimization* (BWM*) to enumerate a gap-free ensemble of conformations in order of increasing energy. Given a branch-decomposition of branch-width w for an n-residue protein design with at most q discrete side-chain conformations per residue, BWM* returns the sparse GMEC in O(nw²q3/2w) time and enumerates each additional conformation in merely O(n log q) time. We define a new measure, Total Effective Search Space (TESS), which can be computed efficiently a priori before BWM* or A* is run. We ran BWM* on 67 protein design problems and found that TESS discriminated between BWM*-efficient and A*- efficient cases with 100% accuracy. As predicted by TESS and validated experimentally, BWM* outperforms A* in 73% of the cases and computes the full ensemble or a close approximation faster than A*, enumerating each additional conformation in milliseconds. Unlike A*, the performance of BWM* can be predicted in polynomial time before running the algorithm, which gives protein designers the power to choose the most efficient algorithm for their particular design problem. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
13. Efficient New Computational Protein Design Algorithms, with Applications to Drug Resistance Prediction and HIV Antibody Design
- Author
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Ojewole, Adegoke and Donald, Bruce R
- Subjects
Protein Design ,Protein Structure ,Bioinformatics ,HIV Antibody Design ,Provable Algorithms ,Binding Affinity Prediction ,Computer science ,Biochemistry ,Drug Resistance Prediction - Abstract
Proteins are essential for myriad biological functions, including DNA replication, molecular transport, catalysis, and antigen recognition. Protein function is determined by three dimensional structure, which is largely determined by amino acid composition. The functional diversity of known proteins suggests that nature can support a much larger set of proteins than is currently available. Protein design aims to explore the space of possible proteins in order to create new proteins with novel or improved biological functions. Two key challenges in protein design, however, are the astronomically large number of possible protein sequences, along with the vast conformation space spanned by each protein. Computational structure-based protein design (CPD) enables the prediction of proteins with desired biochemical properties. A practical CPD method must not only efficiently tackle large sequence and conformation spaces but also use a computationally tractable yet biophysically realistic model of protein plasticity. To this end, I have developed algorithms that accurately and more efficiently search large sequence and conformational spaces to compute proteins that satisfy binding affinity, specificity, and stability requirements. Crucially, my algorithms maintain the state-of-the-art in protein design, namely: provable guarantees, continuous flexibility, and ensemble-based scoring. I applied my algorithms to two biomedically relevant problems: (i) prediction of drug resistance mutations that arise in response to four pre-clinical antibiotics, and (ii) the re-design of a monoclonal HIV antibody for improved potency and breadth of neutralization.
- Published
- 2018
14. Computational Protein Design with Ensembles, Flexibility and Mathematical Guarantees, and its Application to Drug Resistance Prediction, and Antibody Design
- Author
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Gainza Cirauqui, Pablo and Donald, Bruce R.
- Subjects
A* algorithm ,ensemble-based design ,Biophysics ,antibody design ,protein design ,simianization ,Computer science ,Biochemistry ,resistance prediction - Abstract
Proteins are involved in all of life's processes and are also responsible for many diseases. Thus, engineering proteins to perform new tasks could revolutionize many areas of biomedical research. One promising technique for protein engineering is computational structure-based protein design (CSPD). CSPD algorithms search large protein conformational spaces to approximate biophysical quantities. In this dissertation we present new algorithms to realistically and accurately model how amino acid mutations change protein structure. These algorithms model continuous flexibility, protein ensembles and positive/negative design, while providing guarantees on the output. Using these algorithms and the OSPREY protein design program we design and apply protocols for three biomedically-relevant problems: (i) prediction of new drug resistance mutations in bacteria to a new preclinical antibiotic, (ii) the redesign of llama antibodies to potentially reduce their immunogenicity for use in preclinical monkey studies, and (iii) scaffold-based anti-HIV antibody design. Experimental validation performed by our collaborators confirmed the importance of the algorithms and protocols.
- Published
- 2015
15. RNA 3D Structure Analysis and Validation, and Design Algorithms for Proteins and RNA
- Author
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Jain, Swati, Richardson, Jane S, and Donald, Bruce R
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RNA 3D structure validation and correction ,Bioinformatics ,RNA-protein design ,Biophysics ,MolProbity ,Protein design ,Computer science ,OSPREY - Abstract
RNA, or ribonucleic acid, is one of the three biological macromolecule types essential for all known life forms, and is a critical part of a variety of cellular processes. The well known functions of RNA molecules include acting as carriers of genetic information in the form of mRNAs, and then assisting in translation of that information to protein molecules as tRNAs and rRNAs. In recent years, many other kinds of non-coding RNAs have been found, like miRNAs and siRNAs, that are important for gene regulation. Some RNA molecules, called ribozymes, are also known to catalyze biochemical reactions. Functions carried out by these recently discovered RNAs, coupled with the traditionally known functions of tRNAs, mRNAs, and rRNAs make RNA molecules even more crucial and essential components in biology.Most of the functions mentioned above are carried out by RNA molecules associ- ating themselves with proteins to form Ribonucleoprotein (RNP) complexes, e.g. the ribosome or the splicesosome. RNA molecules also bind a variety of small molecules, such as metabolites, and their binding can turn on or off gene expression. These RNP complexes and small molecule binding RNAs are increasingly being recognized as potential therapeutic targets for drug design. The technique of computational structure-based rational design has been successfully used for designing drugs and inhibitors for protein function, but its potential has not been tapped for design of RNA or RNP complexes. For the success of computational structure-based design, it is important to both understand the features of RNA three-dimensional structure and develop new and improved algorithms for protein and RNA design.This document details my thesis work that covers both the above mentioned areas. The first part of my thesis work characterizes and analyzes RNA three-dimensional structure, in order to develop new methods for RNA validation and refinement, and new tools for correction of modeling errors in already solved RNA structures. I collaborated to assemble non-redundant and quality-conscious datasets of RNA crystal structures (RNA09 and RNA11), and I analyzed the range of values occupied by the RNA backbone and base dihedral angles to improve methods for RNA structure correction, validation, and refinement in MolProbity and PHENIX. I rebuilt and corrected the pre-cleaved structure of the HDV ribozyme and parts of the 50S ribosomal subunit to demonstrate the potential of new tools and techniques to improve RNA structures and help crystallographers to make correct biological interpretations. I also extended the previous work of characterizing RNA backbone conformers by the RNA Ontology Consortium (ROC) to define new conformers using the data from the larger RNA11 dataset, supplemented by ERRASER runs that optimize data points to add new conformers or improve cluster separation.The second part of my thesis work develops novel algorithms for structure-basedprotein redesign when interactions between distant residue pairs are neglected and the design problem is represented by a sparse residue interaction graph. I analyzed the sequence and energy differences caused by using sparse residue interaction graphs (using the protein redesign package OSPREY), and proposed a novel use of ensemble-based provable design algorithms to mitigate the effects caused by sparse residue interaction graphs. I collaborated to develop a novel branch-decomposition based dynamic programming algorithm, called BWM*, that returns the Global Minimum Energy Conformation (GMEC) for sparse residue interaction graphs much faster than the traditional A* search algorithm. As the final step, I used the results of my analysis of the RNA base dihedral angle and implemented the capability of RNA design and RNA structural flexibility in osprey. My work enables OSPREY to design not only RNA, but also simultaneously design both the RNA and the protein chains in a RNA-protein interface.
- Published
- 2015
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